Aerial Inspection Behaviors via RL-based Quadrotor Control for Under-canopy Forest Environments
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916025403965440 |
|---|---|
| author | Suarez, Fausto Mauricio Lagos Saradagi, Akshit Sumathy, Vidya Sankaranarayanan, Viswa Narayanan Nikolakopoulos, George |
| author_facet | Suarez, Fausto Mauricio Lagos Saradagi, Akshit Sumathy, Vidya Sankaranarayanan, Viswa Narayanan Nikolakopoulos, George |
| contents | This paper addresses the problem of using a deep Reinforcement Learning (RL)-based low-level Quadrotor controller within an autonomous Quadrotor navigation stack for aerial inspection missions in under-canopy forest environments. Specifically, the article presents an end-to-end (mapping states to RPMs) Quadrotor control policy that achieves inspection view-pose tracking (simultaneous position and yaw reference tracking), which is crucial for various target inspection behaviors and point-to-point navigation in forests. To ensure safe and reliable deployment of the end-to-end RL controller in long-range missions, this article utilizes a higher navigation guidance layer comprising of a Traveling Salesman Problem planner (TSP) and a Rapidly-exploring Random Tree Star (RRT*) planner. Over a known map of a forest and a set of user-specified inspection regions, the TSP planner finds the optimal visitation sequence. Between two target regions, collision-free paths that respect the tracking limitations of the lower end-to-end RL policy are generated by an RRT* planner. Through five target inspection scenarios, this article demonstrates that an RL-based motor-level stabilizing controller, supported by a navigation guidance layer, can be used effectively as the low-level inspection execution module for under-canopy forest inspection missions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_19202 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Aerial Inspection Behaviors via RL-based Quadrotor Control for Under-canopy Forest Environments Suarez, Fausto Mauricio Lagos Saradagi, Akshit Sumathy, Vidya Sankaranarayanan, Viswa Narayanan Nikolakopoulos, George Robotics Artificial Intelligence Optimization and Control This paper addresses the problem of using a deep Reinforcement Learning (RL)-based low-level Quadrotor controller within an autonomous Quadrotor navigation stack for aerial inspection missions in under-canopy forest environments. Specifically, the article presents an end-to-end (mapping states to RPMs) Quadrotor control policy that achieves inspection view-pose tracking (simultaneous position and yaw reference tracking), which is crucial for various target inspection behaviors and point-to-point navigation in forests. To ensure safe and reliable deployment of the end-to-end RL controller in long-range missions, this article utilizes a higher navigation guidance layer comprising of a Traveling Salesman Problem planner (TSP) and a Rapidly-exploring Random Tree Star (RRT*) planner. Over a known map of a forest and a set of user-specified inspection regions, the TSP planner finds the optimal visitation sequence. Between two target regions, collision-free paths that respect the tracking limitations of the lower end-to-end RL policy are generated by an RRT* planner. Through five target inspection scenarios, this article demonstrates that an RL-based motor-level stabilizing controller, supported by a navigation guidance layer, can be used effectively as the low-level inspection execution module for under-canopy forest inspection missions. |
| title | Aerial Inspection Behaviors via RL-based Quadrotor Control for Under-canopy Forest Environments |
| topic | Robotics Artificial Intelligence Optimization and Control |
| url | https://arxiv.org/abs/2605.19202 |